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Percorrer por autor "Sereniski, Gabriela Paola"

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  • Acceleration of the ATLAS-CERN calorimeter data preparation with GPUs
    Publication . Sereniski, Gabriela Paola; Muiño, Patricia Conde; Rufino, José; Gonçalves, Rogério Aparecido
    The High-Luminosity Large Hadron Collider (HL-LHC) will deliver substantially higher numbers of simultaneous proton-proton collisions (events), increasing the volume and frequency of detector readouts and placing growing demands on the ATLAS calorimeter (sub-detectors that measure energy deposits from particle showers) data-preparation pipeline. Bytestream decoding is the first step of this pipeline, responsible for transforming the raw output of the Liquid Argon (LAr) and Tile (TileCal) calorimeters into physics-ready cells. Due to its sequential nature, it may become a significant bottleneck. At the same time, Graphics Processing Units (GPUs) have become increasingly relevant in high-throughput scientific computing, offering massive parallelism and high-memory bandwidth. This dissertation investigates the feasibility of offloading the calorimeter bytestream decoding to GPUs and evaluates the performance of a prototype implementation integrated within the AthenaMT framework. A complete GPU-based pipeline was developed, including bytestream staging, device transfers, parallel fragment parsing and decoding for both LAr and TileCal. The implementation follows a modular CUDA C++ design tailored for parallel scalability. Validation against the CPU reference showed bit-identical agreement across all tested events. Performance measurements using Run-3 data demonstrated an order-of-magnitude improvement: while CPU decoding of a full calorimeter event takes on average 21.1 ms, the GPU prototype achieves 1.30 ms in synchronous and 1.08 ms in asynchronous mode on a constrained A100 MIG slice. These results confirm that massively parallel architectures can effectively accelerate calorimeter data preparation and motivate future accelerator-aware redesigns of the bytestream format for the HL-LHC.
    2026Dissertação de mestrado Acesso aberto Ver mais